Allainclair – Python, Flask, FastAPI

Designing, creating, and maintaining a backend service integrating load boards across North America.

• Designed from scratch and developed REST API service using FastAPI, PostgreSQL, RabbitMQ, Redis, AWS;
• Integrate REST API with different providers (load boards);
• Wrote unit and end-to-end tests;

It is a large social network where any user can be inspired by the pins of the platform.

Allainclair has worked for almost three years as a backend and full-stack engineer maintaining internal tools for the Ads & Growth and Trust and Safety teams.

Main accomplishments:

• created front-end and back-end features for the Pinterest internal tools;
• maintained the reliability and stability of the systems by improving their observability;
• wrote unit and integration tests for frontend (React) and backend (Python);
• designed new features according to product and engineering requirements;
• integrated new features with some internal systems.

Computer Science chair.

Allainclair ministered the following subjects:

• Algorithms and Data Structures;
• Relational Database;
• Multi and Hypermedia Systems;
• Algorithm Analysis and Graph Theory;
• Object-Oriented Programming.

Smart traffic lights (STL) hardware and software, that can sense streets using cameras and act (open/close) autonomously.

Main accomplishments:

• conducted research in traffic optimization area by using smart traffic lights;
• created a traffic simulator using SUMO (Simulation of Urban MObility). The main algorithm on this simulator had 200% to 400% waiting time optimization on light to medium vehicle traffic;
• designed and developed optimization algorithms for smart traffic lights;
• worked closely with hardware and software teams to develop many features of the STL;
• designed and create embedded distributed real-time systems for the STL with a microservice architecture.

It is a startup that advises clients on security issues. It offered a system to analyze social network messages, like Twitter, using Natural Language Process techniques to alert the clients.

Main accomplishments:

• created spam and classifier models using Naive Bayes based on Twitter messages;
• developed clustering models to aggregate similar messages;
• used Python/Scikit-learn and Java to create the machine learning models;
• deployed the models integrating with Elasticsearch and Apache Kafka.